explain ： This is a practical project of machine learning （ Incidental data + Code + file + Video Explanation ）, If you need data + Code + file + Video Explanation You can get it directly at the end of the article .
1. Project background
In the whole process of sewage treatment , In order to better make the treated water meet the environmental protection standards , In every link of sewage treatment, water quality testing machines and equipment will be used to measure the water body , According to the data and information measured by water quality testing machines and equipment , Choose the corresponding disposal method , Make the water quality index of this link meet the requirements , Move on to the next solution .
The project passed PSO Particle swarm optimization support vector machine regression model , Conduct water quality inspection .
2. Data acquisition
The modeling data comes from the network ( Compiled by the author of this project ), The statistics of data items are as follows ：
The details of the data are as follows ( Part of the show )：
3. Data preprocessing
3.1 use Pandas Tool view data
Use Pandas The tool head() Method to view the first five rows of data ：
Key code ：
3.2 Missing data view
Use Pandas The tool info() Method to view data information ：
You can see from the above picture that , All in all 7 A variable , There are no missing values in the data , common 705 Data .
Key code ：
3.3 Descriptive statistics
adopt Pandas The tool describe() Method to see the average of the data 、 Standard deviation 、 minimum value 、 quantile 、 Maximum .
The key codes are as follows ：
4. Exploratory data analysis
4.1 Ammonia nitrogen NH4_N_ET Line chart of variables
use Matplotlib The tool plot() Methods draw a line chart ：
4.2 PH_ET Variable box diagram
use Pandas The tool plot(kind=‘box) Methods draw a histogram ：
As can be seen from the above figure ,PH The mean value is 7.8 about .
4.3 Scatter plot and fitting line of total nitrogen and ammonia nitrogen
use seaborn The tool lmplot () Methods draw the scatter diagram and fitting line ：
As can be seen from the above figure , There is a certain linear relationship between total nitrogen and ammonia nitrogen .
4.4 correlation analysis
As you can see from the above figure , The larger the value, the stronger the correlation , A positive value is a positive correlation 、 A negative value is a negative correlation .PH_ET And labels NH4_N_ET The correlation is very weak , In the subsequent feature engineering, we can consider removing this feature variable .
5. Feature Engineering
5.1 Establish characteristic data and label data
The key codes are as follows ：
5.2 Data set splitting
adopt train_test_split() Method according to 80% Training set 、20% Divide the test set , The key codes are as follows ：
6. structure PSO Particle swarm optimization support vector machine regression model
Particle swarm optimization algorithm (PSO：Particle swarm optimization) It's an evolutionary computing technology （evolutionary computation）. From the study of birds' predation behavior . The basic idea of particle swarm optimization ： It is to find the optimal solution through the cooperation and information sharing among individuals in the group .
The main use of PSO Particle swarm optimization SVR Algorithm , For target regression .
6.1 Build the model with default parameters
6.2 PSO The optimal parameters for particle swarm optimization
Key code ：
Optimal parameters ：
6.3 The optimal parameter value constructs the model
7. Model to evaluate
7.1 Evaluation indicators and results
The evaluation index mainly includes the interpretable variance value 、 Mean absolute error 、 Mean square error 、R Square value, etc .
As can be seen from the table above ,R Party for 0.6752 Better than the default parameters ; The interpretable variance is 0.6777 Better than the default parameters , The optimized regression model has good effect , On this basis, you can increase the number of particle swarm iterations for tuning , The model evaluation value here is iteration 1 Evaluation value of times , Generally, we have to iterate 100 Time to 1000 Time , Therefore, you can modify the parameter value as needed in practical application .
The key codes are as follows ：
7.2 Comparison between real value and predicted value
It can be seen from the above figure that the fluctuations of the real value and the predicted value are basically the same , The fitting effect of the model is good .
8. Conclusion and Prospect
in summary , This paper adopts PSO Particle swarm optimization algorithm to find support vector machine SVR The optimal parameter value of the algorithm is used to build the regression model , Finally, it is proved that the model we proposed is effective . This model can be used to predict daily products .
The materials needed for the actual combat of this machine learning project , The project resources are as follows ：
Project description ：
link ：https://pan.baidu.com/s/1dW3S1a6KGdUHK90W-lmA4w
Extraction code ：bcbpIf the network disk fails , You can add blogger wechat ：zy10178083
